21 July 2017 A study on the relationship between urban roads and car fuel consumption based on the ST-matching algorithm
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042019 (2017) https://doi.org/10.1117/12.2281784
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Since the study of fuel consumptions is of great importance and the related data is accessible, many researches about factors affecting fuel consumptions have appeared.To sum up, the driving style of drivers, automobile emissions and the type of the routes are the mainly three factors.Classification is relatively single.In order to improve the classification accuracy’ this text studies that there exist some special roads,when drivers drive through them,their fuel consumptions will be similar because of road qualities.To achieve this goal, the first step is to calculate fuel consumptions per 100km of all city roads.Recognizing and examining special roads based on the ST-Matching algorithm.Third is to analyze road qualities of special roads like the length of roads, the speed of driving, and compare them with other common roads.Then we choose 4 cases to analyze. We find that when driving through special roads, fuel consumptions would be similar because of road qualities.Besides, the average length of special roads is longer than common roads’ and the mean velocity, the speed of getting and off special roads are faster than common roads’.The findings in this paper can filter out special road segments as noisy data in the study of relationship between driving styles and fuel consumptions,and it also has very high practical significance on recommending fuel-efficient paths.
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Mingshan Liu, Mingshan Liu, Wenbo Zhang, Wenbo Zhang, Yuan Zhou, Yuan Zhou, Yanqin Xun, Yanqin Xun, Rui Wang, Rui Wang, } "A study on the relationship between urban roads and car fuel consumption based on the ST-matching algorithm", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042019 (21 July 2017); doi: 10.1117/12.2281784; https://doi.org/10.1117/12.2281784
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